The impact of industrial agglomeration on the synergistic evolution of the energy big data ecosystem: empirical findings from China
Bibliographic record
Abstract
Whether industrial agglomeration can promote the synergistic evolution of the energy big data ecosystem (EBDE) is important for effectively solving environmental pollution problems, promoting clean technology innovation, and achieving sustainable development of the energy industry. To this end, this paper applies the Harken model to construct EBDE synergy indicators from a synergistic perspective in 30 Chinese provinces from 2014 to 2020. The EBDE core subsystem is the sequential covariate, which plays a decisive factor in the synergistic evolution of EBDE. The synergy value of the system in each province shows a significant upward trend, and the overall synergy condition is significantly improved. On this basis, this paper explores how industrial agglomeration affects the synergistic evolution of the energy big data ecosystem. The results of the econometric study show that industrial agglomeration at the national level contributes to the enhancement of CEBDE. However, the impact of industrial agglomeration on CEBDE varies significantly at the regional level. In the central region, industrial agglomeration can contribute to the enhancement of CEBDE, but in the eastern and western regions, the industrial agglomeration has no significant effect on CEBDE. Finally, based on the study, this paper proposes specific recommendations for improving CEBDE. These results have important implications for the formulation of energy-use policies and the realization of sustainable energy development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".